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# Authors:                                                                                                     #
# Kenny Young (kjyoung@ualberta.ca)                                                                            #
# Tian Tian (ttian@ualberta.ca)                                                                                #
################################################################################################################
import numpy as np


#####################################################################################################################
# Env
#
# The player controls a paddle on the bottom of the screen and must bounce a ball tobreak 3 rows of bricks along the 
# top of the screen. A reward of +1 is given for each brick broken by the ball.  When all bricks are cleared another 3 
# rows are added. The ball travels only along diagonals, when it hits the paddle it is bounced either to the left or 
# right depending on the side of the paddle hit, when it hits a wall or brick it is reflected. Termination occurs when
# the ball hits the bottom of the screen. The balls direction is indicated by a trail channel.
#
#####################################################################################################################
class Env:
    def __init__(self, ramping = None, seed = None):
        self.channels ={
            'paddle':0,
            'ball':1,
            'trail':2,
            'brick':3,
        }
        self.action_map = ['n','l','u','r','d','f']
        self.random = np.random.RandomState(seed)
        self.reset()

    # Update environment according to agent action
    def act(self, a):
        r = 0
        if(self.terminal):
            return r, self.terminal
            
        a = self.action_map[a]

        # Resolve player action
        if(a=='l'):
            self.pos = max(0, self.pos-1)
        elif(a=='r'):
            self.pos = min(9,self.pos+1)

        # Update ball position
        self.last_x = self.ball_x
        self.last_y = self.ball_y
        if(self.ball_dir == 0):
            new_x = self.ball_x-1
            new_y = self.ball_y-1
        elif(self.ball_dir == 1):
            new_x = self.ball_x+1
            new_y = self.ball_y-1
        elif(self.ball_dir == 2):
            new_x = self.ball_x+1
            new_y = self.ball_y+1
        elif(self.ball_dir == 3):
            new_x = self.ball_x-1
            new_y = self.ball_y+1

        strike_toggle = False
        if(new_x<0 or new_x>9):
            if(new_x<0):
                new_x = 0
            if(new_x>9):
                new_x=9
            self.ball_dir=[1,0,3,2][self.ball_dir]
        if(new_y<0):
            new_y = 0
            self.ball_dir=[3,2,1,0][self.ball_dir]
        elif(self.brick_map[new_y,new_x]==1):
            strike_toggle = True
            if(not self.strike):
                r+=1
                self.strike = True
                self.brick_map[new_y,new_x]=0
                new_y = self.last_y
                self.ball_dir=[3,2,1,0][self.ball_dir]
        elif(new_y == 9):
            if(np.count_nonzero(self.brick_map)==0):
                self.brick_map[1:4,:] = 1
            if(self.ball_x == self.pos):
                self.ball_dir=[3,2,1,0][self.ball_dir]
                new_y = self.last_y
            elif(new_x == self.pos):
                self.ball_dir=[2,3,0,1][self.ball_dir]
                new_y = self.last_y
            else:
                self.terminal = True

        if(not strike_toggle):
            self.strike = False

        self.ball_x = new_x
        self.ball_y = new_y
        return r, self.terminal

    # Query the current level of the difficulty ramp, difficulty does not ramp in this game, so return None
    def difficulty_ramp(self):
        return None  

    # Process the game-state into the 10x10xn state provided to the agent and return
    def state(self):
        state = np.zeros((10,10,len(self.channels)))
        state[self.ball_y,self.ball_x,self.channels['ball']] = 1
        state[9,self.pos, self.channels['paddle']] = 1
        state[self.last_y,self.last_x,self.channels['trail']] = 1
        state[:,:,self.channels['brick']] = self.brick_map
        return state

    # Reset to start state for new episode
    def reset(self):
        self.ball_y = 3
        ball_start = self.random.choice(2)
        self.ball_x, self.ball_dir = [(0,2),(9,3)][ball_start]
        self.pos = 4
        self.brick_map = np.zeros((10,10))
        self.brick_map[1:4,:] = 1
        self.strike = False
        self.last_x = self.ball_x
        self.last_y = self.ball_y
        self.terminal = False

    # Dimensionality of the game-state (10x10xn)
    def state_shape(self):
        return [10,10,len(self.channels)]

    # Subset of actions that actually have a unique impact in this environment
    def minimal_action_set(self):
        minimal_actions = ['n','l','r']
        return [self.action_map.index(x) for x in minimal_actions]
